Lead Data Scientist + Instructor

About Newton: Newton School of Technology (NST) is a new-age institution redefining technical education in India. Founded by IIT alumni, NST offers a 4-year B.Tech in Computer Science and AI, focused on hands-on learning and deep industry integration. Within two years, over 93% of students have secured paid internships with companies like Razorpay, SarvamAI, and DRDO, along with global exposure through tech treks to Singapore and Silicon Valley. Led by a distinguished faculty comprising ICPC World Finalists and ex-professionals from ISRO, Google, MakeMyTrip, and several other leading tech organisations, NST is building a scalable, high-impact model that produces industry-ready talent for the world's most advanced technology roles. About the Role: We are currently looking for a Data Scientist + Instructor– AI/ML to join our Computer Science Department. This role is ideal for professionals with substantial industry experience who are passionate about Artificial Intelligence and Machine Learning and committed to shaping the next generation of tech talent. The position combines hands-on technical expertise with academic responsibilities, including designing and delivering course content, conducting practical lab sessions, and mentoring students in core Data Science and AI/ML concepts. The ideal candidate will also collaborate with academic leaders and industry partners to ensure curriculum relevance, integrate real-world problem-solving, and drive strong learning outcomes. Key Responsibilities: ● Teach Applied AI/ML: Design and deliver practical, project-based courses in AI/ML (Python for ML, Statistics, ML Algorithms, Deep Learning, NLP, CV, ML Ops, GenAI). ● Develop Industry-Relevant Curriculum: Help design and update the AI/ML curriculum to reflect current industry tools, techniques, and best practices, incorporating your professional experience and case studies. ● Mentor Student Projects: Guide students through hands-on AI/ML projects, providing technical direction, code reviews, and feedback based on industry standards. ● Guide & Mentor Students: Advise students on developing practical skills, understanding career paths in AI/ML, and preparing for internships and job placements. ● Stay Current: Bring the latest AI/ML research, tools, and industry trends into the classroom. ● Collaborate: Work closely with other expert faculty and staff to create a unified and effective learning experience. ● Assess Practical Skills: Design and evaluate assignments, projects, and assessments focused on real-world applications. Qualifications and Requirements: ● Bachelor's or Master's degree in Computer Science, Engineering, Data Science, AI/ML, or related field. (PhD is valued but not mandatory.) ● 7+ years of direct, hands-on professional experience in the tech industry as an AI/ML Engineer, Data Scientist, Research Scientist, or similar role involving AI/ML development and deployment. ● Proven Industry Track Record: Demonstrated experience in building, training, and deploying machine learning models (including deep learning) for real-world problems. ● Deep AI/ML Understanding: Strong grasp of core ML algorithms (classical & deep learning- CNNs, RNNs, Transformers), model evaluation, statistics, and awarenessof current research/industry trends. ● Passion for Teaching/Mentoring: Ability to explain complex concepts clearly and guide others. Prior mentoring, corporate training, technical workshops, or project supervision experience is highly relevant. (Formal academic teaching experience is not mandatory.) Required Skills: ● Technical: Expert-level Python programming. ● Proficiency with data science libraries (Pandas, NumPy, Scikit-learn). ● Hands-on experience with ML/DL frameworks (TensorFlow, PyTorch). ● Strong SQL and data handling skills. ● Understanding of ML Ops practices and tools (Git, Docker, AWS/GCP/Azure). ● Knowledge of key AI areas (NLP, Computer Vision, Generative AI/LLMs). ● Soft Skills: Strong communication, mentoring ability, collaboration, and a genuine passion for education. Good-to-Have: ● Prior teaching experience at the undergraduate or graduate level. ● Familiarity with modern teaching methodologies and academic.